1. Field of the Invention
The present invention relates generally to equipment and process monitoring, and more particularly to monitoring systems instrumented with sensors that measure correlated phenomena. The present invention further relates to modeling instrumented, real-time processes using the aggregate sensor information to ascertain information about the state of the process, and a method of training an empirical model used therein.
2. Description of the Related Art
Conventional methods are known for monitoring equipment or processes—generically “systems”—using sensors to measure operational parameters of the system. The data values from sensors can be observed directly to understand how the system is functioning. Alternatively, for unattended operation, it is known to compare sensor data values against stored or predetermined thresholds in an automated fashion, and generate an exception condition or alarm requiring human intervention only when a sensor datum value exceeds a corresponding threshold.
A number of problems exist with monitoring systems using thresholds. One problem is the difficulty of selecting a threshold for a dynamic parameter that avoids a burdensome number of false alarms, yet catches real alarms and provides sufficient warning to take corrective action when a system parameter—as measured by a sensor—moves outside of acceptable operation. Another problem is posed by sensor failure, which may result in spurious parameter values. It may not be clear from a sensor data value that the sensor has failed. Such a failure can entirely undermine monitoring of the subject system.
In systems with a plurality of sensors measuring correlated phenomena in the system, it is known to use certain methods to consider all sensors in aggregate to overcome some of these problems. By observing the behavior of all the sensor data values in aggregate, it can be possible to dramatically improve monitoring without suffering unduly from false and missed alarms. Also, knowledge of how all the correlated parameters behave in unison can help determine that a sensor has failed, when isolated monitoring of data from that sensor would not in and of itself indicate the sensor failure.
Known methods for viewing aggregate sensor data typically employ a modeling function that embodies prior knowledge of the system. One such technique known as a “first-principles” model requires a well-defined mathematical description of the dynamics of the system selecting system snapshots taken at minimum and maximum system parameter excursions. The mathematical model is used as a reference against which current aggregate sensor data can be compared to view nascent problems or sensor failures. However, this technique is particularly vulnerable to even the slightest structural change in the observed system and may not provide sufficient system characterization in operating regions where system parameters vary most dynamically. The mathematical model of the system is often very costly to obtain, and in many cases, may not be reasonably possible at all.
Another class of techniques involves empirically modeling the system as a “black box”, without discerning any specific mechanics within the system. System modeling using such techniques can be easier and more resilient in the face of structural system changes. Modeling in these techniques typically involves providing some historic sensor data corresponding to desired or normal system operation, which is then used to “train” the model.
One particular technique is described in U.S. Pat. No. 5,987,399, the teachings of which are incorporated herein by reference. As taught therein, sensor data is gathered from a plurality of sensors measuring correlated parameters of a system in a desired operating state. This data is used to derive an empirical model comprising certain acceptable historical system states. Real-time sensor data from the system is provided to a modeling engine embodying the empirical model, which computes a measure of the similarity of the real-time state to all prior known acceptable states in the model. From that measure of similarity, an estimate is generated for expected sensor data values. The real-time sensor data and the estimated inspected sensor data are compared, and if there is a discrepancy, corrective action can be taken.
Other empirical model-based monitoring systems are disclosed in U.S. Pat. No. 4,937,763 to Mott, wherein learned observations are employed in a system state analyzer, and U.S. Pat. No. 5,764,509 to Gross et al., the teachings of which are hereby incorporated by reference. Selection of the appropriate historical sensor data for generating any of these empirical models is a serious hurdle. The models variously rely on the historic data accurately representing the “normal” conditions of the process or machine being monitored. Therefore, one must ensure that the data collected as historic data corresponds to an acceptable state of operation, and not one in which a latent fault was present in the process or machine. A larger problem is then to ensure that the historic data is sufficiently representative of the expected ranges of operation, so that the empirical model does not generate alarms for states of operation it has no history for, but which are otherwise acceptable states for the process or machine. It is critical to the success of the empirical model for monitoring that the collected sensor data be properly distilled or condensed to a trained set of data that adequately represents the knowledge of the normal states of operation of the process or machine being monitored. An additional problem is that, since empirical modeling methods can be computationally demanding, it is often preferable to restrict the historic data on which they are built or trained to a minimum, in order to reduce training time and required computing power. Finally, some empirical models are actually adversely affected by too much training data: They tend to find every current state of the monitored process or machine acceptable, because something close enough to it can be found in the historic data. Therefore, a successful selection of representative “training set” data must not result in an “overtrained” model.
In U.S. Pat. No. 5,764,609 to Gross et al., a training method for selecting observations of time-correlated sensor data called Mim-Max is presented. According to this way of training a model, the collected normal sensor data is condensed or distilled down to a “training set” by selecting those observations (or “snapshots”) that contain a global maximum or minimum for a sensor with respect to all values taken on by that sensor across the entire collected sensor data. Thus, as a maximum the number of observations that are included in the training set that results from the training is twice the number of sensors being modeled. While this method assures the inclusion of extrema for all sensors in the model, it may be desirable to enhance the model with inclusion of other snapshots with intermediate values.
Therefore, when selecting vector-arranged snapshot data for inclusion in a training set for deriving an empirical model, there is a need for selecting an optimized training set that best characterizes the dynamics of the underlying machine or process. There is a further need for a method for selecting historic data that minimizes the size of the training set. Finally, there is a need for training methods that are computationally efficient and fast. This invention achieves these benefits by automating selection in a way that maximizes the data membership from regions of great dynamics, while keeping the overall training set size manageable.